1. Field of the Invention
The present invention relates to a method for detecting image noise, and more particularly, to a method for detecting image noise according to spatial and temporal information of the image.
2. Description of the Related Art
In conventional applications, the detection of image noise is often divided into two categories. The first is to detect in the spatial domain and the second is to detect in the temporal domain. The image noise estimated in the spatial domain is often referred to as the spatial noise of the image because it is detected by using spatial information only. Similarly, the image noise estimated in the temporal domain is referred to as temporal noise of the image because it is detected by using temporal information only.
The above-mentioned spatial information represents information carried by a same frame. For example, the spatial information can be 1-D information (i.e., the information of the same scan line) or 2-D information (i.e., the information of multiple scan lines). On the other hand, the temporal information represents the information relating different frames.
However, disadvantages are accompanied with the above-mentioned two detecting categories. Take the spatial noise as an example, it is nearly impossible to differentiate the detected spatial noise originating from real noise or from a high-frequency portion of the original image. Since as mentioned previously, the spatial noise is detected by using spatial information only. Similarly, it is impossible to determine whether the detected temporal noise originated from real noise or from a moving object in the original image. Since the temporal noise is detected by using temporal information only. Therefore, the temporal noise or the spatial noise, may in fact not be noise and thereby making the detection incorrect.
The detection result of the spatial noise is quite essential for the image processing operation. Detecting incorrect results may further ruin (i.e., degrade) the image quality instead of improving it. In other words, the image may suffer from pronounced distortions due to the incorrect detection result.